IoT and Fog-Computing-Based Predictive Maintenance Model for Effective Asset Management in Industry 4.0 Using Machine Learning

The assets in Industry 4.0 are categorised into physical, virtual and human. The innovation and popularisation of ubiquitous computing enhance the usage of smart devices: RFID tags, QR codes, LoRa tags, etc. for assets identification and tracking. The generated data from Industrial Internet of Things (IIoT) eases information visibility and process automation in Industry 4.0. Virtual assets include the data produced from IIoT. One of the applications of the industrial big data is to predict the failure of manufacturing equipment. Predictive maintenance enables the business owner to decide such as repairing or replacing the component before an actual failure which affects the whole production line. Therefore, Industry 4.0 requires an effective asset management to optimise the tasks distributions and predictive maintenance model. This paper presents the Genetic Algorithm (GA) based resource management integrating with machine learning for predictive maintenance in fog computing. The time, cost and energy performance of GA along with MinMin, MaxMin, FCFS, RoundRobin are simulated in the FogWorkflowsim. The predictive maintenance model is built in two-class logistic regression using real-time datasets. The results demonstrate that the proposed technique outperforms MinMin, MaxMin, FCFS, RoundRobin in execution time, cost and energy usage. The execution time is 0.48%faster, 5.43% lower cost and energy usage is 28.10% lower in comparison with second-best results. The training and testing accuracy of the prediction model is 95.1% and 94.5%, respectively

HealthFaaS: AI based Smart Healthcare System for Heart Patients using Serverless Computing

Heart disease is one of the leading causes of death worldwide, and with early detection, mortality rates can be reduced. Well-known studies have shown that the latest Artificial Intelligence (AI) can be used to determine the risk of heart disease. However, existing studies did not consider dynamic scalability to get the best performance from these AI models in case of an increasing number of users. To solve this problem, we proposed an AI-powered smart healthcare framework called HealthFaaS, using the Internet of Things (IoT) and a Serverless Computing environment to reduce heart disease-related deaths and prevent financial losses by reducing misdiagnoses. HealthFaaS framework collects health data from users via IoT devices and sends it to AI models deployed on a Google Cloud Platform (GCP) based serverless computing environment due to its advantages such as dynamic scalability, less operational complexity, and a pay-as-you-go pricing model. The performance of five different AI models for heart disease risk detection is evaluated and compared based on key parameters such as accuracy, precision, recall, F-Score, and AUC. Experimental results demonstrate that the LightGBM model gives the highest success in detecting heart diseases with an accuracy rate of 91.80%. Further, we have tested the performance of the HealthFaaS framework in terms of Quality of Service (QoS) parameters such as throughput and latency against the increasing number of users and compared it with a non-serverless platform. In addition, we have also evaluated the cold start latency using a serverless platform which determined that the amount of memory and the software language makes a direct impact on the cold start latency

ATOM: AI-Powered Sustainable Resource Management for Serverless Edge Computing Environments

Serverless edge computing decreases unnecessary resource usage on end devices with limited processing power and storage capacity. Despite its benefits, serverless edge computing's zero scalability is the major source of the cold start delay, which is yet unsolved. This latency is unacceptable for time-sensitive Internet of Things (IoT) applications like autonomous cars. Most existing approaches need containers to idle and use extra computing resources. Edge devices have fewer resources than cloud-based systems, requiring new sustainable solutions. Therefore, we propose an AI-powered, sustainable resource management framework called ATOM for serverless edge computing. ATOM utilizes a deep reinforcement learning model to predict exactly when cold start latency will happen. We create a cold start dataset using a heart disease risk scenario and deploy using Google Cloud Functions. To demonstrate the superiority of ATOM, its performance is compared with two different baselines, which use the warm-start containers and a two-layer adaptive approach. The experimental results showed that although the ATOM required more calculation time of 118.76 seconds, it performed better in predicting cold start than baseline models with an RMSE ratio of 148.76. Additionally, the energy consumption and $CO_{2}$ emission amount of these models are evaluated and compared for the training and prediction phases

Uniform electron gases

We show that the traditional concept of the uniform electron gas (UEG) --- a homogeneous system of finite density, consisting of an infinite number of electrons in an infinite volume --- is inadequate to model the UEGs that arise in finite systems. We argue that, in general, a UEG is characterized by at least two parameters, \textit{viz.} the usual one-electron density parameter $\rho$ and a new two-electron parameter $\eta$. We outline a systematic strategy to determine a new density functional $E(\rho,\eta)$ across the spectrum of possible $\rho$ and $\eta$ values.Comment: 8 pages, 2 figures, 5 table

Arthroscopic Treatment of Acetabular Retroversion With Acetabuloplasty and Subspine Decompression: A Matched Comparison With Patients Undergoing Arthroscopic Treatment for Focal Pincer-Type Femoroacetabular Impingement.

BackgroundGlobal acetabular retroversion is classically treated with open reverse periacetabular osteotomy. Given the low morbidity and recent success associated with the arthroscopic treatment of femoroacetabular impingement (FAI), there may also be a role for arthroscopic treatment of acetabular retroversion. However, the safety and outcomes after hip arthroscopic surgery for retroversion need further study, and the effect of impingement from the anterior inferior iliac spine (subspine) in patients with retroversion is currently unknown.HypothesisArthroscopic treatment for global acetabular retroversion will be safe, and patients will have similar outcomes compared with a matched group undergoing arthroscopic treatment for focal pincer-type FAI.Study designCohort study; Level of evidence, 2.MethodsPatients undergoing hip arthroscopic surgery for symptomatic global acetabular retroversion were prospectively enrolled and compared with a matched group of patients undergoing arthroscopic surgery for focal pincer-type FAI. Both groups underwent the same arthroscopic treatment protocol. All patients were administered patient-reported outcome (PRO) measures, including the 12-item Short-Form Health Survey (SF-12) Physical Component Summary (PCS) and a Mental Component Summary (MCS), modified Harris Hip Score (mHHS), Hip disability and Osteoarthritis Outcome Score (HOOS), and visual analog scale (VAS) for pain preoperatively and at 1 year postoperatively.ResultsThere were no differences in age, sex, or body mass index between 39 hips treated for global acetabular retroversion and 39 hips treated for focal pincer-type FAI. There were no major or minor complications in either group. Patients who underwent arthroscopic treatment for global acetabular retroversion demonstrated similar significant improvements in postoperative PRO scores (scores increased by 17 to 43 points) as patients who underwent arthroscopic treatment for focal pincer-type FAI. Patients treated for retroversion who also underwent subspine decompression had greater improvement than patients who did not undergo subspine decompression for the HOOS-Pain (33.7 ± 15.3 vs 22.5 ± 17.6, respectively; P = .046) and HOOS-Quality of Life (49.7 ± 18.8 vs 34.6 ± 22.0, respectively; P = .030) scores.ConclusionArthroscopic treatment for acetabular retroversion is safe and provides significant clinical improvement similar to arthroscopic treatment for pincer-type FAI. Patients with acetabular retroversion who also underwent arthroscopic subspine decompression demonstrated greater improvements in pain and quality of life outcomes than those who underwent arthroscopic treatment without subspine decompression

Addressing triggering post zancolli lasso procedure

A claw hand causes disability as kinematics are affected due to hyperextension at the metacarpophalangeal joints. Zancolli lasso procedure is a simple tenodesis procedure which effectively lessens clawing to allow better grip. We present a56-year-old lady who had a history of trauma with progressive clawing of her left hand. She has been diagnosed with partially recovered incomplete lower trunk brachial plexus injury. She underwent successful Zancolli lasso procedures for all of her fingers but 8 months later, the patient developed triggering of the index and middle fingers. We experimented by releasing the adhesions in one finger and releasing the whole A1 pulley together with the lasso-ed flexor digitorum superficialis (FDS) in the other finger and the latter worked.We repeated the procedure in the index finger and the triggering resolved. Although both her index and middle fingers now have a flexor digitorum profundus (FDP) only (the FDS having retracted proximally), she did not have a recurrence of her clawing. We attributed the triggering due to increasing A1 pulley volume as well as contractures causing post-release functional positions

Why do Particle Clouds Generate Electric Charges?

Grains in desert sandstorms spontaneously generate strong electrical charges; likewise volcanic dust plumes produce spectacular lightning displays. Charged particle clouds also cause devastating explosions in food, drug and coal processing industries. Despite the wide-ranging importance of granular charging in both nature and industry, even the simplest aspects of its causes remain elusive, because it is difficult to understand how inert grains in contact with little more than other inert grains can generate the large charges observed. Here, we present a simple yet predictive explanation for the charging of granular materials in collisional flows. We argue from very basic considerations that charge transfer can be expected in collisions of identical dielectric grains in the presence of an electric field, and we confirm the model's predictions using discrete-element simulations and a tabletop granular experiment

Random Numbers Certified by Bell's Theorem

Randomness is a fundamental feature in nature and a valuable resource for applications ranging from cryptography and gambling to numerical simulation of physical and biological systems. Random numbers, however, are difficult to characterize mathematically, and their generation must rely on an unpredictable physical process. Inaccuracies in the theoretical modelling of such processes or failures of the devices, possibly due to adversarial attacks, limit the reliability of random number generators in ways that are difficult to control and detect. Here, inspired by earlier work on nonlocality based and device independent quantum information processing, we show that the nonlocal correlations of entangled quantum particles can be used to certify the presence of genuine randomness. It is thereby possible to design of a new type of cryptographically secure random number generator which does not require any assumption on the internal working of the devices. This strong form of randomness generation is impossible classically and possible in quantum systems only if certified by a Bell inequality violation. We carry out a proof-of-concept demonstration of this proposal in a system of two entangled atoms separated by approximately 1 meter. The observed Bell inequality violation, featuring near-perfect detection efficiency, guarantees that 42 new random numbers are generated with 99% confidence. Our results lay the groundwork for future device-independent quantum information experiments and for addressing fundamental issues raised by the intrinsic randomness of quantum theory.Comment: 10 pages, 3 figures, 16 page appendix. Version as close as possible to the published version following the terms of the journa

Superluminal motion of a relativistic jet in the neutron star merger GW170817

The binary neutron star merger GW170817 was accompanied by radiation across the electromagnetic spectrum and localized to the galaxy NGC 4993 at a distance of 41+/-3 Mpc. The radio and X-ray afterglows of GW170817 exhibited delayed onset, a gradual rise in the emission with time as t^0.8, a peak at about 150 days post-merger, followed by a relatively rapid decline. To date, various models have been proposed to explain the afterglow emission, including a choked-jet cocoon and a successful-jet cocoon (a.k.a. structured jet). However, the observational data have remained inconclusive as to whether GW170817 launched a successful relativistic jet. Here we show, through Very Long Baseline Interferometry, that the compact radio source associated with GW170817 exhibits superluminal motion between two epochs at 75 and 230 days post-merger. This measurement breaks the degeneracy between the models and indicates that, while the early-time radio emission was powered by a wider-angle outflow (cocoon), the late-time emission was most likely dominated by an energetic and narrowly-collimated jet, with an opening angle of <5 degrees, and observed from a viewing angle of about 20 degrees. The imaging of a collimated relativistic outflow emerging from GW170817 adds substantial weight to the growing evidence linking binary neutron star mergers and short gamma-ray bursts.Comment: 42 pages, 4 figures (main text), 2 figures (supplementary text), 2 tables. Referee and editor comments incorporate